Data Augmentation for Imbalanced Regression
Abstract
In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates. Such a situation can lead to biases in the estimates. In this case, we propose a data augmentation algorithm that combines a weighted resampling (WR) and a data augmentation (DA) procedure. In a first step, the DA procedure permits exploring a wider support than the initial one. In a second step, the WR method drives the exogenous distribution to a target one. We discuss the choice of the DA procedure through a numerical study that illustrates the advantages of this approach. Finally, an actuarial application is studied.
Keywords
Cite
@article{arxiv.2302.09288,
title = {Data Augmentation for Imbalanced Regression},
author = {Samuel Stocksieker and Denys Pommeret and Arthur Charpentier},
journal= {arXiv preprint arXiv:2302.09288},
year = {2023}
}
Comments
paper accepted at the AISTATS 2023 conference, to be published in PMLR (Proceedings of Machine Learning Research)